Motor Speech Conference
This is the code for the statistical analysis for Motor Speech Conference 2022.
Loading Packages
library(rio)
library(tidyverse)
library(irr)
library(performance)
library(car)
library(ggpubr)
library(corrr)
library(ggridges)
library(furniture)
Upload Datasets
Reliability <- rio::import("Prepped Data/Reliability Data.csv")
AcousticData <- rio::import("Prepped Data/AcousticMeasures.csv")
AcousticData <- AcousticData %>%
dplyr::filter(!grepl("_rel", Speaker)) %>%
dplyr::select(c(Speaker, Sex, Etiology, vowel_ED_b, VSA_b, Hull_b, Hull_bVSD_25, Hull_bVSD_50, Hull_bVSD_75, VAS, transAcc))
Listeners <- rio::import("Prepped Data/Listener_Demographics.csv") %>%
dplyr::select(!c(StartDate:proloficID, Q2.4_6_TEXT, Q3.2_8_TEXT, AudioCheck:EP3))
Listeners$race[Listeners$Q3.3_7_TEXT == "Native American/ African amercing"] <- "Biracial or Multiracial"Inter-rater Reliability
Two raters (the first two authors) completed vowel segmentation for the speakers. To calculate inter-rater reliability, 20% of the speakers were segmented again by the other rater. Two-way intraclass coefficients were computed for the extracted F1 and F2 from the temporal midpoint of the vowel segments. Since only one set of ratings will be used in the data analysis, we focus on the single ICC results and interpretation. However, we also report the average ICC values to be comprehensive.
## Creating new data frames to calculate ICC for extracted F1 and F2 values
F1_Rel <- Reliability %>%
dplyr::select(c(F1, F1_rel))
F2_Rel <- Reliability %>%
dplyr::select(c(F2, F2_rel))
## Single ICC for F1
Single_F1 <- irr::icc(F1_Rel, model = "twoway", type = "agreement", unit = "single")
## Average ICC for F1
Average_F1 <- irr::icc(F1_Rel, model = "twoway", type = "agreement", unit = "average")
## Single ICC for F2
Single_F2 <- irr::icc(F2_Rel, model = "twoway", type = "agreement", unit = "single")
## Average ICC for F2
Average_F2 <- irr::icc(F2_Rel, model = "twoway", type = "agreement", unit = "average")
## Inter-rater reliability results and interpretation
print(paste("Single ICC for F1 is ", round(Single_F1$value, digits = 3), ". ",
"The 95% CI is [", round(Single_F1$lbound, digits = 3), " - ", round(Single_F1$ubound, digits = 3), "].", sep = ""))[1] "Single ICC for F1 is 0.866. The 95% CI is [0.837 - 0.89]."print(paste("Single ICC for F2 is ", round(Single_F2$value, digits = 3), ". ",
"The 95% CI is [", round(Single_F2$lbound, digits = 3), " - ", round(Single_F2$ubound, digits = 3), "].", sep = ""))[1] "Single ICC for F2 is 0.931. The 95% CI is [0.916 - 0.944]."print(paste("Average ICC for F1 is ", round(Average_F1$value, digits = 3), ". ",
"The 95% CI is [", round(Average_F1$lbound, digits = 3), " - ", round(Average_F1$ubound, digits = 3), "].", sep = ""))[1] "Average ICC for F1 is 0.928. The 95% CI is [0.911 - 0.942]."print(paste("Average ICC for F2 is ", round(Average_F2$value, digits = 3), ". ",
"The 95% CI is [", round(Average_F2$lbound, digits = 3), " - ", round(Average_F2$ubound, digits = 3), "].", sep = ""))[1] "Average ICC for F2 is 0.964. The 95% CI is [0.956 - 0.971]."print("Thus, interrater reliability for the extracted F1 and F2 values from the vowel segments was good to excellent.")[1] "Thus, interrater reliability for the extracted F1 and F2 values from the vowel segments was good to excellent."## Removing extra data frames from environment
rm(F1_Rel, F2_Rel, Reliability, Single_F1, Single_F2, Average_F1, Average_F2)
Descriptive Statistics
Means and SD
Descriptives <- AcousticData %>%
dplyr::group_by(Sex, Etiology) %>%
dplyr::summarize(VSA_mean = mean(VSA_b, na.rm =T), VSA_sd = sd(VSA_b, na.rm = T),
Disp_mean = mean(vowel_ED_b, na.rm =T), Disp_sd = sd(vowel_ED_b, na.rm =T),
Hull_mean = mean(Hull_b, na.rm =T), Hull_sd = sd(Hull_b, na.rm =T),
VSD25_mean = mean(Hull_bVSD_25, na.rm =T), VSD25_sd = sd(Hull_bVSD_25, na.rm =T),
VSD50_mean = mean(Hull_bVSD_50, na.rm =T), VSD50_sd = sd(Hull_bVSD_50, na.rm =T),
VSD75_mean = mean(Hull_bVSD_75, na.rm =T), VSD75_sd = sd(Hull_bVSD_75, na.rm =T),
VAS_mean = mean(VAS, na.rm =T), VAS_sd = sd(VAS, na.rm =T),
OT_mean = mean(transAcc, na.rm =T), OT_sd = sd(transAcc, na.rm =T))`summarise()` has grouped output by 'Sex'. You can override using the `.groups` argument.DescriptivesbySex <- AcousticData %>%
dplyr::group_by(Sex) %>%
dplyr::summarize(VSA_mean = mean(VSA_b, na.rm =T), VSA_sd = sd(VSA_b, na.rm = T),
Disp_mean = mean(vowel_ED_b, na.rm =T), Disp_sd = sd(vowel_ED_b, na.rm =T),
Hull_mean = mean(Hull_b, na.rm =T), Hull_sd = sd(Hull_b, na.rm =T),
VSD25_mean = mean(Hull_bVSD_25, na.rm =T), VSD25_sd = sd(Hull_bVSD_25, na.rm =T),
VSD50_mean = mean(Hull_bVSD_50, na.rm =T), VSD50_sd = sd(Hull_bVSD_50, na.rm =T),
VSD75_mean = mean(Hull_bVSD_75, na.rm =T), VSD75_sd = sd(Hull_bVSD_75, na.rm =T),
VAS_mean = mean(VAS, na.rm =T), VAS_sd = sd(VAS, na.rm =T),
OT_mean = mean(transAcc, na.rm =T), OT_sd = sd(transAcc, na.rm =T))
DescriptivesbyEtiology <- AcousticData %>%
dplyr::group_by(Etiology) %>%
dplyr::summarize(VSA_mean = mean(VSA_b, na.rm =T), VSA_sd = sd(VSA_b, na.rm = T),
Disp_mean = mean(vowel_ED_b, na.rm =T), Disp_sd = sd(vowel_ED_b, na.rm =T),
Hull_mean = mean(Hull_b, na.rm =T), Hull_sd = sd(Hull_b, na.rm =T),
VSD25_mean = mean(Hull_bVSD_25, na.rm =T), VSD25_sd = sd(Hull_bVSD_25, na.rm =T),
VSD50_mean = mean(Hull_bVSD_50, na.rm =T), VSD50_sd = sd(Hull_bVSD_50, na.rm =T),
VSD75_mean = mean(Hull_bVSD_75, na.rm =T), VSD75_sd = sd(Hull_bVSD_75, na.rm =T),
VAS_mean = mean(VAS, na.rm =T), VAS_sd = sd(VAS, na.rm =T),
OT_mean = mean(transAcc, na.rm =T), OT_sd = sd(transAcc, na.rm =T))
Descriptives
DescriptivesbySex
DescriptivesbyEtiologyNACorrelations among variables
CorrMatrix1 <- AcousticData %>%
dplyr::select(VSA_b, vowel_ED_b, Hull_b, Hull_bVSD_25, Hull_bVSD_50, Hull_bVSD_75, VAS, transAcc) %>%
corrr::correlate()
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'CorrMatrix1NAData Vis
Group Comparisons: Ridgeline Plots
# Vowel Space Area Distribution by Etiology
VSA_plot <- AcousticData %>%
ggplot() +
aes(x = VSA_b,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Area (Bark)") +
theme_classic()
## Corner Dispersion Distribution by Etiology
disp_plot <- AcousticData %>%
ggplot() +
aes(x = vowel_ED_b,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Corner Dispersion (Bark)") +
theme_classic()
# Vowel Space Hull Distribution by Etiology
Hull_plot <- AcousticData %>%
ggplot() +
aes(x = Hull_b,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Hull (Bark)") +
theme_classic()
# Vowel Space Density 25 Distribution by Etiology
vsd_25_plot <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_25,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Denisty 25 (Bark)") +
theme_classic()
# Vowel Space Density 50 Distribution by Etiology
vsd_50_plot <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_50,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Denisty 50 (Bark)") +
theme_classic()
# Vowel Space Density 75 Distribution by Etiology
vsd_75_plot <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_75,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
expand_limits(x=0) +
xlab("Vowel Space Denisty 75 (Bark)") +
theme_classic()
# Visual Analog Scale Intelligibility Rating Distribution by Etiology
VAS_plot <- AcousticData %>%
ggplot() +
aes(x = VAS,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Visual Analog Scale (VAS)") +
theme_classic()
# Orthographic Transcription Score Distribution by Etiology
OT_plot <- AcousticData %>%
ggplot() +
aes(x = transAcc,
y = Etiology,
color = Etiology,
fill = Etiology) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
coord_cartesian(xlim = c(0, 100)) +
xlab("Orthographic Transcription Scores") +
theme_classic()
# Creating Distributions Figure
Distributions <- ggpubr::ggarrange(VSA_plot, disp_plot, Hull_plot, vsd_25_plot, vsd_50_plot, vsd_75_plot, VAS_plot, OT_plot,
ncol = 2,
nrow = 4)Picking joint bandwidth of 0.71
Picking joint bandwidth of 0.166
Picking joint bandwidth of 4.07
Picking joint bandwidth of 3.52
Picking joint bandwidth of 1.99
Picking joint bandwidth of 0.805
Picking joint bandwidth of 11.2
Picking joint bandwidth of 9.57# Saving Distribution Figure
ggsave("Plots/Distribution.png", plot = last_plot(), width = 10, height = 10, unit = "in")
Scatter Plots
# OT Scatterplots
OT_VSA <- AcousticData %>%
ggplot() +
aes(x = VSA_b,
y = transAcc) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#E69F00", fill = "light grey") +
xlab(expression("Vowel Space Area (Bark"^2*")")) +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
OT_disp <- AcousticData %>%
ggplot() +
aes(x = vowel_ED_b,
y = transAcc) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#56B4E9", fill = "light grey") +
xlab("Corner Dispersion (Bark)") +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
OT_Hull <- AcousticData %>%
ggplot() +
aes(x = Hull_b,
y = transAcc) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#009E73", fill = "light grey") +
xlab(expression("Vowel Space Hull (Bark"^2*")")) +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
OT_vsd25 <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_25,
y = transAcc) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#0072B2", fill = "light grey") +
xlab(expression("Vowel Space Density"[25]*" (Bark"^2*")")) +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
OT_vsd75 <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_75,
y = transAcc) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#D55E00", fill = "light grey") +
xlab(expression("Vowel Space Density"[75]*" (Bark"^2*")")) +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
# Creating OT Scatterplot Figure
OT_Scatterplots <- ggpubr::ggarrange(OT_VSA, OT_disp, OT_Hull, OT_vsd25, OT_vsd75,
ncol = 3, nrow = 2)`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'## Saving OT Scatterplot Figure
ggsave("Plots/OT_Scatterplots.png", plot = last_plot(), width = 15, height = 15, unit = "in", scale = 0.6)
# VAS Scatterplots
VAS_VSA <- AcousticData %>%
ggplot() +
aes(x = VSA_b,
y = VAS) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#E69F00", fill = "light grey") +
xlab(expression("Vowel Space Area (Bark"^2*")")) +
ylab("VAS Rating") +
coord_cartesian(ylim = c(0,100)) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
VAS_disp <- AcousticData %>%
ggplot() +
aes(x = vowel_ED_b,
y = VAS) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#56B4E9", fill = "light grey") +
xlab("Corner Dispersion (Bark)") +
ylab("VAS Rating") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
VAS_Hull <- AcousticData %>%
ggplot() +
aes(x = Hull_b,
y = VAS) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#009E73", fill = "light grey") +
xlab(expression("Vowel Space Hull (Bark"^2*")")) +
ylab("VAS Rating") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
VAS_vsd25 <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_25,
y = VAS) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#0072B2", fill = "light grey") +
xlab(expression("Vowel Space Density"[25]*" (Bark"^2*")")) +
ylab("VAS Rating") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
VAS_vsd75 <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_75,
y = VAS) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#D55E00", fill = "light grey") +
xlab(expression("Vowel Space Density"[75]*" (Bark"^2*")")) +
ylab("VAS Rating") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
# Creating VAS Scatterplot Figure
VAS_Scatterplots <- ggpubr::ggarrange(VAS_VSA, VAS_disp, VAS_Hull, VAS_vsd25, VAS_vsd75,
ncol = 3, nrow = 2)
## Saving VAS Scatterplot Figure
ggsave("Plots/VAS_Scatterplots.png", plot = last_plot(), width = 15, height = 15, unit = "in", scale = .6)
OT ~ VAS Plot
# VAS ~ OT Plot
VAS_OT <- AcousticData %>%
ggplot() +
aes(x = VAS,
y = transAcc) +
geom_point() +
geom_smooth(method = "lm", se = T, color = "#CC79A7", fill = "light grey") +
ggtitle("Relationship Between Intelligibility Measures") +
xlab("VAS Rating") +
ylab("Percent Words Correct") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
# Saving OT ~ VAS Plot
ggsave("Plots/OTvas.png", plot = last_plot(), width = 8, height = 8, unit = "in", scale = 0.6)
# OT ~ VAS by Sex
VAS_OT_Sex <- AcousticData %>%
ggplot() +
aes(x = VAS,
y = transAcc,
color = Sex) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("VAS Rating") +
ylab("Percent Words Correct") +
theme_classic()
ggsave("Plots/OTvas_sex.png", plot = last_plot(), width = 5, height = 5, unit = "in", scale = 0.6)
# OT ~ VAS by Etiology
VAS_OT_Etiology <- AcousticData %>%
ggplot() +
aes(x = VAS,
y = transAcc,
color = Etiology) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("VAS Rating") +
ylab("Percent Words Correct") +
theme_classic()
ggsave("Plots/OTvas_etiology.png", plot = last_plot(), width = 5, height = 5, unit = "in", scale = 0.6)
Group Comparisons
VSA
## Specify the Model
VSA_group <- aov(VSA_b ~ Etiology, data = AcousticData)
## Assumption Check
plot(VSA_group, 1)
plot(VSA_group, 2)
car::leveneTest(VSA_group)group coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 3 0.5761 0.6344
36 VSA_residuals <- residuals(object = VSA_group)
shapiro.test(VSA_residuals)
Shapiro-Wilk normality test
data: VSA_residuals
W = 0.89408, p-value = 0.001292## Model Results
summary(VSA_group) Df Sum Sq Mean Sq F value Pr(>F)
Etiology 3 31.6 10.532 2.795 0.0541 .
Residuals 36 135.7 3.768
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1## Kruskal-Wallis Test
kruskal.test(VSA_b ~ Etiology, data = AcousticData)
Kruskal-Wallis rank sum test
data: VSA_b by Etiology
Kruskal-Wallis chi-squared = 8.978, df = 3, p-value = 0.02958## Pairwise Comparisons
pairwise.wilcox.test(AcousticData$VSA_b, AcousticData$Etiology, p.adjust.method = "bonferroni")
Pairwise comparisons using Wilcoxon rank sum exact test
data: AcousticData$VSA_b and AcousticData$Etiology
ALS Ataxic HD
Ataxic 0.069 - -
HD 0.054 1.000 -
PD 1.000 0.738 0.738
P value adjustment method: bonferroni Corner Dispersion
## Specify the Model
disp_group <- aov(vowel_ED_b ~ Etiology, data = AcousticData)
## Assumption Check
plot(disp_group, 1)
plot(disp_group, 2)
car::leveneTest(disp_group)group coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 3 0.5502 0.6513
36 disp_residuals <- residuals(object = disp_group)
shapiro.test(disp_residuals)
Shapiro-Wilk normality test
data: disp_residuals
W = 0.98237, p-value = 0.7765## Model Results
summary(disp_group) Df Sum Sq Mean Sq F value Pr(>F)
Etiology 3 0.768 0.2559 1.749 0.174
Residuals 36 5.267 0.1463 Hull
## Specify the Model
hull_group <- aov(Hull_b ~ Etiology, data = AcousticData)
## Assumption Check
plot(hull_group, 1)
plot(hull_group, 2)
car::leveneTest(hull_group)group coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 3 2.5009 0.07492 .
36
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1hull_residuals <- residuals(object = hull_group)
shapiro.test(hull_residuals)
Shapiro-Wilk normality test
data: hull_residuals
W = 0.96952, p-value = 0.3474## Model Results
summary(hull_group) Df Sum Sq Mean Sq F value Pr(>F)
Etiology 3 446.2 148.72 2.093 0.118
Residuals 36 2558.2 71.06 VSD 25
## Specify the Model
vsd25_group <- aov(Hull_bVSD_25 ~ Etiology, data = AcousticData)
## Assumption Check
plot(vsd25_group, 1)
plot(vsd25_group, 2)
car::leveneTest(vsd25_group)group coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 3 0.1853 0.9057
36 vsd25_residuals <- residuals(object = vsd25_group)
shapiro.test(vsd25_residuals)
Shapiro-Wilk normality test
data: vsd25_residuals
W = 0.95627, p-value = 0.1247## Model Summary
summary(vsd25_group) Df Sum Sq Mean Sq F value Pr(>F)
Etiology 3 133.3 44.42 0.977 0.414
Residuals 36 1636.6 45.46 VSD 50
## Specify the Model
vsd50_group <- aov(Hull_bVSD_50 ~ Etiology, data = AcousticData)
## Assumption Check
plot(vsd50_group, 1)
plot(vsd50_group, 2)
car::leveneTest(vsd50_group)group coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 3 1.0748 0.372
36 vsd50_residuals <- residuals(object = vsd50_group)
shapiro.test(vsd50_residuals)
Shapiro-Wilk normality test
data: vsd50_residuals
W = 0.92221, p-value = 0.009043## Model Results
summary(vsd50_group) Df Sum Sq Mean Sq F value Pr(>F)
Etiology 3 65.2 21.74 1.149 0.342
Residuals 36 681.0 18.92 ## Kruskal Wallis Test
kruskal.test(Hull_bVSD_50 ~ Etiology, data = AcousticData)
Kruskal-Wallis rank sum test
data: Hull_bVSD_50 by Etiology
Kruskal-Wallis chi-squared = 3.341, df = 3, p-value = 0.342VSD 75
## Specify the Model
vsd75_group <- aov(Hull_bVSD_75 ~ Etiology, data = AcousticData)
## Assumption Check
plot(vsd75_group, 1)
plot(vsd75_group, 2)
car::leveneTest(vsd75_group)group coerced to factor.Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 3 0.8627 0.4693
36 vsd75_residuals <- residuals(object = vsd75_group)
shapiro.test(vsd75_residuals)
Shapiro-Wilk normality test
data: vsd75_residuals
W = 0.84762, p-value = 7.879e-05## Model Summary
summary(vsd75_group) Df Sum Sq Mean Sq F value Pr(>F)
Etiology 3 17.35 5.783 1.148 0.343
Residuals 36 181.31 5.036 ## Kruskal Wallis
kruskal.test(Hull_bVSD_75 ~ Etiology, data = AcousticData)
Kruskal-Wallis rank sum test
data: Hull_bVSD_75 by Etiology
Kruskal-Wallis chi-squared = 3.7127, df = 3, p-value = 0.2942VAS
## Specify the Model
VAS_group <- aov(VAS ~ Sex*Etiology, data = AcousticData)
## Assumption Check
plot(VAS_group, 1)
plot(VAS_group, 2)
car::leveneTest(VAS_group)Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 7 0.654 0.7084
32 VAS_residuals <- residuals(object = VAS_group)
shapiro.test(VAS_residuals)
Shapiro-Wilk normality test
data: VAS_residuals
W = 0.94633, p-value = 0.05674## Model Results
summary(VAS_group) Df Sum Sq Mean Sq F value Pr(>F)
Sex 1 501 500.6 0.722 0.402
Etiology 3 2975 991.5 1.431 0.252
Sex:Etiology 3 1253 417.8 0.603 0.618
Residuals 32 22179 693.1 OT
## Specify the Model
OT_group <- aov(transAcc ~ Sex*Etiology, data = AcousticData)
## Assumption Check
plot(OT_group, 1)
plot(OT_group, 2)
car::leveneTest(OT_group)Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 7 0.5655 0.7781
32 OT_residuals <- residuals(object = OT_group)
shapiro.test(OT_residuals)
Shapiro-Wilk normality test
data: OT_residuals
W = 0.93272, p-value = 0.01978## Model Results
summary(OT_group) Df Sum Sq Mean Sq F value Pr(>F)
Sex 1 415 414.9 0.685 0.414
Etiology 3 2170 723.5 1.195 0.327
Sex:Etiology 3 337 112.2 0.185 0.906
Residuals 32 19377 605.5 Modeling Intelligibility
Orthographic Transcriptions
Model 1
# Specifying Model 1
OT_Model1 <- lm(transAcc ~ Hull_bVSD_25, data = AcousticData)
## Model 1 Assumptions
performance::check_model(OT_Model1)
## Model 1 Summary
summary(OT_Model1)
Call:
lm(formula = transAcc ~ Hull_bVSD_25, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.896 -14.090 5.996 17.470 36.105
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.2973 9.7372 4.960 1.5e-05 ***
Hull_bVSD_25 0.6417 0.5663 1.133 0.264
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.83 on 38 degrees of freedom
Multiple R-squared: 0.03268, Adjusted R-squared: 0.007224
F-statistic: 1.284 on 1 and 38 DF, p-value: 0.2643Model 2
## Specifying Model 2
OT_Model2 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)
## Model 2 Assumption Check
performance::check_model(OT_Model2)
## Model 2 Summary
summary(OT_Model2)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-48.854 -13.962 5.567 16.758 36.257
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.3374 10.3316 4.582 5.09e-05 ***
Hull_bVSD_25 0.8045 0.7781 1.034 0.308
Hull_bVSD_75 -0.7188 2.3225 -0.309 0.759
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.11 on 37 degrees of freedom
Multiple R-squared: 0.03518, Adjusted R-squared: -0.01698
F-statistic: 0.6745 on 2 and 37 DF, p-value: 0.5156## Model 1 and Model 2 Comparison
anova(OT_Model1, OT_Model2)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75
Res.Df RSS Df Sum of Sq F Pr(>F)
1 38 21570
2 37 21514 1 55.696 0.0958 0.7587Model 3
## Specifying Model 3
OT_Model3 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, data = AcousticData)
## Model 3 Assumption Check
performance::check_model(OT_Model3)
## Model 3 Summary
summary(OT_Model3)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b,
data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-56.706 -13.157 7.018 17.957 29.990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.0806 14.4916 2.145 0.0388 *
Hull_bVSD_25 -0.8439 1.2984 -0.650 0.5199
Hull_bVSD_75 0.3159 2.3714 0.133 0.8948
Hull_b 1.2941 0.8247 1.569 0.1253
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.65 on 36 degrees of freedom
Multiple R-squared: 0.09695, Adjusted R-squared: 0.0217
F-statistic: 1.288 on 3 and 36 DF, p-value: 0.2932## Model 2 and Model 3 Comparison
anova(OT_Model2, OT_Model3)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 37 21514
2 36 20137 1 1377.5 2.4626 0.1253Model 4
## Specifying Model 4
OT_Model4 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b, data = AcousticData)
## Model 4 Assumption Check
performance::check_model(OT_Model4)
## Model 4 Summary
summary(OT_Model4)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-44.796 -11.144 3.042 12.567 34.297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.4533 13.1156 2.093 0.04365 *
Hull_bVSD_25 -1.2602 1.1782 -1.070 0.29214
Hull_bVSD_75 0.5663 2.1390 0.265 0.79274
Hull_b 0.7364 0.7654 0.962 0.34261
VSA_b 6.0896 1.9954 3.052 0.00432 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.32 on 35 degrees of freedom
Multiple R-squared: 0.2868, Adjusted R-squared: 0.2052
F-statistic: 3.518 on 4 and 35 DF, p-value: 0.01626## Model 3 and Model 4 Comparison
anova(OT_Model3, OT_Model4)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 36 20137
2 35 15904 1 4232.4 9.314 0.004321 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Model 5
## Specifying Model 5
OT_Model5 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b, data = AcousticData)
## Model 4 Assumption Check
performance::check_model(OT_Model5)
## Model 4 Summary
summary(OT_Model5)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-46.533 -11.028 3.327 13.017 33.227
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.3598 21.5072 0.947 0.3505
Hull_bVSD_25 -1.2567 1.1924 -1.054 0.2994
Hull_bVSD_75 0.7007 2.1882 0.320 0.7508
Hull_b 0.6895 0.7826 0.881 0.3845
VSA_b 5.3903 2.6194 2.058 0.0473 *
vowel_ED_b 5.5182 13.1650 0.419 0.6777
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.57 on 34 degrees of freedom
Multiple R-squared: 0.2904, Adjusted R-squared: 0.1861
F-statistic: 2.783 on 5 and 34 DF, p-value: 0.03271## Model 3 and Model 4 Comparison
anova(OT_Model4, OT_Model5)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 35 15904
2 34 15823 1 81.764 0.1757 0.6777Final Model
## Specifying Final Model
OT_Model_final <- lm(transAcc ~ VSA_b, data = AcousticData)
## Final Model Assumption Check
performance::check_model(OT_Model_final)
## Final Model Summary
summary(OT_Model_final)
Call:
lm(formula = transAcc ~ VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-46.72 -12.69 2.97 14.37 35.39
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.508 7.857 4.138 0.000187 ***
VSA_b 5.872 1.613 3.641 0.000807 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 20.86 on 38 degrees of freedom
Multiple R-squared: 0.2586, Adjusted R-squared: 0.2391
F-statistic: 13.25 on 1 and 38 DF, p-value: 0.0008068VAS Models
Model 1
# Specifying Model 1
VAS_Model1 <- lm(VAS ~ Hull_bVSD_25, data = AcousticData)
## Model 1 Assumptions
performance::check_model(VAS_Model1)
## Model 1 Summary
summary(VAS_Model1)
Call:
lm(formula = VAS ~ Hull_bVSD_25, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.625 -16.684 8.462 19.440 37.352
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.6328 10.7512 3.965 0.000313 ***
Hull_bVSD_25 0.5877 0.6253 0.940 0.353236
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26.31 on 38 degrees of freedom
Multiple R-squared: 0.02272, Adjusted R-squared: -0.003001
F-statistic: 0.8833 on 1 and 38 DF, p-value: 0.3532Model 2
## Specifying Model 2
VAS_Model2 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)
## Model 2 Assumption Check
performance::check_model(VAS_Model2)
## Model 2 Summary
summary(VAS_Model2)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.850 -16.576 8.382 19.448 37.237
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.3384 11.4211 3.707 0.000684 ***
Hull_bVSD_25 0.6376 0.8602 0.741 0.463195
Hull_bVSD_75 -0.2204 2.5674 -0.086 0.932036
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26.66 on 37 degrees of freedom
Multiple R-squared: 0.02291, Adjusted R-squared: -0.0299
F-statistic: 0.4338 on 2 and 37 DF, p-value: 0.6513## Model 1 and Model 2 Comparison
anova(VAS_Model1, VAS_Model2)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75
Res.Df RSS Df Sum of Sq F Pr(>F)
1 38 26296
2 37 26291 1 5.239 0.0074 0.932Model 3
## Specifying Model 3
VAS_Model3 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, data = AcousticData)
## Model 3 Assumption Check
performance::check_model(VAS_Model3)
## Model 3 Summary
summary(VAS_Model3)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-56.444 -18.989 6.121 18.036 32.281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.0400 16.0599 1.559 0.128
Hull_bVSD_25 -1.1164 1.4389 -0.776 0.443
Hull_bVSD_75 0.8805 2.6280 0.335 0.740
Hull_b 1.3770 0.9139 1.507 0.141
Residual standard error: 26.21 on 36 degrees of freedom
Multiple R-squared: 0.08087, Adjusted R-squared: 0.00428
F-statistic: 1.056 on 3 and 36 DF, p-value: 0.3799## Model 2 and Model 3 Comparison
anova(VAS_Model2, VAS_Model3)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 37 26291
2 36 24731 1 1559.6 2.2702 0.1406Model 4
## Specifying Model 4
VAS_Model4 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b, data = AcousticData)
## Model 4 Assumption Check
performance::check_model(VAS_Model4)
## Model 4 Summary
summary(VAS_Model4)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b,
data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.25 -14.17 5.76 16.26 41.48
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.0802 14.5922 1.445 0.15746
Hull_bVSD_25 -1.5708 1.3109 -1.198 0.23885
Hull_bVSD_75 1.1539 2.3798 0.485 0.63078
Hull_b 0.7682 0.8516 0.902 0.37320
VSA_b 6.6479 2.2200 2.995 0.00502 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.72 on 35 degrees of freedom
Multiple R-squared: 0.2683, Adjusted R-squared: 0.1847
F-statistic: 3.209 on 4 and 35 DF, p-value: 0.02405## Model 3 and Model 4 Comparison
anova(VAS_Model3, VAS_Model4)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 36 24731
2 35 19687 1 5043.9 8.9671 0.00502 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Model 5
## Specifying Model 5
VAS_Model5 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b, data = AcousticData)
## Model 5 Assumption Check
performance::check_model(VAS_Model5)
## Model 5 Summary
summary(VAS_Model5)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-44.569 -13.697 5.187 16.183 40.204
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.6419 23.9197 0.529 0.6006
Hull_bVSD_25 -1.5667 1.3261 -1.181 0.2456
Hull_bVSD_75 1.3137 2.4337 0.540 0.5928
Hull_b 0.7124 0.8704 0.818 0.4188
VSA_b 5.8159 2.9132 1.996 0.0539 .
vowel_ED_b 6.5644 14.6417 0.448 0.6568
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.99 on 34 degrees of freedom
Multiple R-squared: 0.2726, Adjusted R-squared: 0.1657
F-statistic: 2.549 on 5 and 34 DF, p-value: 0.0461## Model 4 and Model 5 Comparison
anova(VAS_Model4, VAS_Model5)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 35 19687
2 34 19572 1 115.7 0.201 0.6568Final Model
## Specifying Final Model
VAS_Model_final <- lm(VAS ~ VSA_b, data = AcousticData)
## Final Model Assumption Check
performance::check_model(VAS_Model_final)
## Final Model Summary
summary(VAS_Model_final)
Call:
lm(formula = VAS ~ VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-44.956 -15.943 6.754 17.153 43.062
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.703 8.761 2.820 0.00760 **
VSA_b 6.163 1.798 3.427 0.00148 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.26 on 38 degrees of freedom
Multiple R-squared: 0.2361, Adjusted R-squared: 0.216
F-statistic: 11.74 on 1 and 38 DF, p-value: 0.001482Relationship between OT and VAS
Tested Model
# Specify Model
OT_VAS_model <- lm(transAcc ~ VAS*Etiology + VAS*Sex, data = AcousticData)
# Assumption Check
performance::check_model(OT_VAS_model)
# Model Results
summary(OT_VAS_model)
Call:
lm(formula = transAcc ~ VAS * Etiology + VAS * Sex, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-14.910 -4.525 -1.280 5.529 16.932
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.8993562 5.7092981 1.909 0.0659 .
VAS 0.8924319 0.1276625 6.991 9.1e-08 ***
EtiologyAtaxic 2.9854239 9.5149402 0.314 0.7559
EtiologyHD 2.1009983 7.3948950 0.284 0.7783
EtiologyPD -2.4903757 10.1590855 -0.245 0.8080
SexM 6.8939642 7.0796700 0.974 0.3380
VAS:EtiologyAtaxic -0.0019088 0.1791048 -0.011 0.9916
VAS:EtiologyHD 0.0007804 0.1453318 0.005 0.9958
VAS:EtiologyPD 0.0320470 0.1732105 0.185 0.8545
VAS:SexM -0.1220661 0.1241230 -0.983 0.3333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 8.488 on 30 degrees of freedom
Multiple R-squared: 0.9031, Adjusted R-squared: 0.874
F-statistic: 31.06 on 9 and 30 DF, p-value: 8.181e-13Final Model
# Specify Final Model
OT_VAS_final <- lm(transAcc ~ VAS, data = AcousticData)
# Model Results
summary(OT_VAS_final)
Call:
lm(formula = transAcc ~ VAS, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-13.6882 -4.9316 -0.4408 4.9974 17.2110
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.74548 2.78681 4.932 1.64e-05 ***
VAS 0.86093 0.04799 17.938 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.873 on 38 degrees of freedom
Multiple R-squared: 0.8944, Adjusted R-squared: 0.8916
F-statistic: 321.8 on 1 and 38 DF, p-value: < 2.2e-16Alternate Analysis
In this alternate analysis, we are looking at the relationship between these acoustic measures with speech intelligibility for the ALS/PD and the HD/Ataxic speakers separately. We create a new variable called Incoord, where the ALS/PD Speakers are set as the reference group (in order to compare to the Ataxic/HD Speaker Group). Group Comparisons, additional data visualizations, and further linear model comparisons are completed.
Data Prep
# Creating a new variable in AcousticData. Incoord is a dummy variable. ALS/PD Speakers = 0, HD/Ataxic = 1
AcousticData <- AcousticData %>%
dplyr::mutate(Incoord = case_when(Etiology == "HD" ~ 1,
Etiology == "Ataxic" ~ 1,
TRUE ~ 0)) %>%
dplyr::mutate(Incoord = as.factor(Incoord))
Descriptives
Descriptives_ALS.PD_incoord <- AcousticData %>%
dplyr::group_by(Incoord) %>%
dplyr::summarize(VSA_mean = mean(VSA_b, na.rm =T), VSA_sd = sd(VSA_b, na.rm = T),
Disp_mean = mean(vowel_ED_b, na.rm =T), Disp_sd = sd(vowel_ED_b, na.rm =T),
Hull_mean = mean(Hull_b, na.rm =T), Hull_sd = sd(Hull_b, na.rm =T),
VSD25_mean = mean(Hull_bVSD_25, na.rm =T), VSD25_sd = sd(Hull_bVSD_25, na.rm =T),
VSD50_mean = mean(Hull_bVSD_50, na.rm =T), VSD50_sd = sd(Hull_bVSD_50, na.rm =T),
VSD75_mean = mean(Hull_bVSD_75, na.rm =T), VSD75_sd = sd(Hull_bVSD_75, na.rm =T),
VAS_mean = mean(VAS, na.rm =T), VAS_sd = sd(VAS, na.rm =T),
OT_mean = mean(transAcc, na.rm =T), OT_sd = sd(transAcc, na.rm =T))
Descriptives_ALS.PD_incoordNAData Vis
Group Comparison Ridgeline Plots
# VSA Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
VSA_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = VSA_b,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Area (Bark)") +
theme_classic()
# Corner Dispersion Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
disp_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = vowel_ED_b,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Corner Dispersion (Bark)") +
theme_classic()
# Hull Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
hull_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = Hull_b,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Hull (Bark)") +
theme_classic()
# VSD 25 Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
vsd25_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_25,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Desnity 25 (Bark)") +
theme_classic()
# VSD 50 Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
vsd50_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_50,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Desnity 50 (Bark)") +
theme_classic()
# VSD 75 Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
vsd75_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_75,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Vowel Space Desnity 75 (Bark)") +
theme_classic()
# Orthographic Transcription Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
OT_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = transAcc,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Percent Words Correct") +
theme_classic()
# Visual Analog Scale Intelligibility Rating Ridgeline Plot by Incoordination (Incoord = 0 are ALS/PD Speakers, Incoord == 1 are HD/Ataxic Speakers)
VAS_incoord.plot <- AcousticData %>%
ggplot() +
aes(x = VAS,
y = Incoord,
color = Incoord,
fill = Incoord) +
geom_density_ridges(jittered_points = T,
position = position_points_jitter(width = 0.01, height = 0),
point_shape = '|',
point_size = 5,
point_alpha = 1,
alpha = 0.7,
scale = .7) +
xlab("Ratigns") +
theme_classic()
# Creating Distributions by Incoord Figure
Distributions_incoord <- ggpubr::ggarrange(VSA_incoord.plot, disp_incoord.plot, hull_incoord.plot, vsd25_incoord.plot, vsd50_incoord.plot, vsd75_incoord.plot, OT_incoord.plot, VAS_incoord.plot,
ncol = 2,
nrow = 4)Picking joint bandwidth of 0.637
Picking joint bandwidth of 0.144
Picking joint bandwidth of 3.74
Picking joint bandwidth of 3.27
Picking joint bandwidth of 1.93
Picking joint bandwidth of 0.764
Picking joint bandwidth of 10.1
Picking joint bandwidth of 13# Saving Distribution Figure
ggsave("Plots/Distribution_incoord.png", plot = last_plot(), width = 10, height = 10, unit = "in")
Scatterplots
# OT Scatterplots by Incoord
OT_VSA.incoord <- AcousticData %>%
ggplot() +
aes(x = VSA_b,
y = transAcc,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Area (Bark)") +
ylab("Percent Words Correct") +
ggtitle("Orthographic Transcription") +
coord_cartesian(ylim = c(0,100)) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
OT_disp.incoord <- AcousticData %>%
ggplot() +
aes(x = vowel_ED_b,
y = transAcc,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Corner Dispersion (Bark)") +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
OT_Hull.incoord <- AcousticData %>%
ggplot() +
aes(x = Hull_b,
y = transAcc,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Hull (Bark)") +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
OT_vsd25.incoord <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_25,
y = transAcc,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Density 25 (Bark)") +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
OT_vsd75.incoord <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_75,
y = transAcc,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Density 75 (Bark)") +
ylab("Percent Words Correct") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
# VAS Scatterplots
VAS_VSA.incoord <- AcousticData %>%
ggplot() +
aes(x = VSA_b,
y = VAS,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Area (Bark)") +
ylab("VAS Score") +
ggtitle("Visual Analog Scale") +
coord_cartesian(ylim = c(0,100)) +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
VAS_disp.incoord <- AcousticData %>%
ggplot() +
aes(x = vowel_ED_b,
y = VAS,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Corner Dispersion (Bark)") +
ylab("VAS Score") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
VAS_Hull.incoord <- AcousticData %>%
ggplot() +
aes(x = Hull_b,
y = VAS,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Hull (Bark)") +
ylab("VAS Score") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
VAS_vsd25.incoord <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_25,
y = VAS,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Density 25 (Bark)") +
ylab("VAS Score") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
VAS_vsd75.incoord <- AcousticData %>%
ggplot() +
aes(x = Hull_bVSD_75,
y = VAS,
color = Incoord) +
geom_point() +
geom_smooth(method = "lm", se = F) +
xlab("Vowel Space Density 75 (Bark)") +
ylab("VAS Score") +
coord_cartesian(ylim = c(0,100)) +
theme_classic()
# Creating Scatterplot Figure
Scatterplots_incoord <- ggpubr::ggarrange(OT_VSA.incoord, VAS_VSA.incoord, OT_disp.incoord, VAS_disp.incoord, OT_Hull.incoord, VAS_Hull.incoord, OT_vsd25.incoord, VAS_vsd25.incoord, OT_vsd75.incoord, VAS_vsd75.incoord,
ncol = 2,
nrow = 5)`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'## Saving Scatterplot Figure
ggsave("Plots/Scatterplots_incoord.png", plot = last_plot(), width = 15, height = 20, unit = "in", scale = .5)
Group Comparisons
Two Dataframes for Incoord Groups
coord.group <- AcousticData %>%
dplyr::filter(Incoord == 0)
incoord.group <- AcousticData %>%
dplyr::filter(Incoord == 1)
VSA
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(VSA_b[Incoord == 0]))
Shapiro-Wilk normality test
data: VSA_b[Incoord == 0]
W = 0.94206, p-value = 0.2622with(AcousticData, shapiro.test(VSA_b[Incoord == 1]))
Shapiro-Wilk normality test
data: VSA_b[Incoord == 1]
W = 0.81843, p-value = 0.001648## Equal Variance Check
res.ftest.VSA <- var.test(VSA_b ~ Incoord, data = AcousticData)
res.ftest.VSA
F test to compare two variances
data: VSA_b by Incoord
F = 0.66898, num df = 19, denom df = 19, p-value = 0.3888
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.2647915 1.6901522
sample estimates:
ratio of variances
0.6689828 # Model Results
VSA_b_t <- t.test(incoord.group$VSA_b, coord.group$VSA_b, var.equal = T)
VSA_b_t
Two Sample t-test
data: incoord.group$VSA_b and coord.group$VSA_b
t = 2.8889, df = 38, p-value = 0.006352
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.519310 2.951478
sample estimates:
mean of x mean of y
5.289125 3.553732 Corner Dispersion
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(vowel_ED_b[Incoord == 0]))
Shapiro-Wilk normality test
data: vowel_ED_b[Incoord == 0]
W = 0.93799, p-value = 0.2197with(AcousticData, shapiro.test(vowel_ED_b[Incoord == 1]))
Shapiro-Wilk normality test
data: vowel_ED_b[Incoord == 1]
W = 0.9837, p-value = 0.9725## Equal Variance Check
res.ftest.disp <- var.test(vowel_ED_b ~ Incoord, data = AcousticData)
res.ftest.disp
F test to compare two variances
data: vowel_ED_b by Incoord
F = 1.2328, num df = 19, denom df = 19, p-value = 0.6528
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4879679 3.1146765
sample estimates:
ratio of variances
1.232827 # Model Results
disp_t <- t.test(incoord.group$vowel_ED_b, coord.group$vowel_ED_b, var.equal = T)
disp_t
Two Sample t-test
data: incoord.group$vowel_ED_b and coord.group$vowel_ED_b
t = 2.031, df = 38, p-value = 0.04929
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.000790061 0.485404644
sample estimates:
mean of x mean of y
2.165220 1.922123 Hull
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(Hull_b[Incoord == 0]))
Shapiro-Wilk normality test
data: Hull_b[Incoord == 0]
W = 0.93152, p-value = 0.1652with(AcousticData, shapiro.test(Hull_b[Incoord == 1]))
Shapiro-Wilk normality test
data: Hull_b[Incoord == 1]
W = 0.96708, p-value = 0.6923## Equal Variance Check
res.ftest.hull <- var.test(Hull_b ~ Incoord, data = AcousticData)
res.ftest.hull
F test to compare two variances
data: Hull_b by Incoord
F = 1.7881, num df = 19, denom df = 19, p-value = 0.2144
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.7077388 4.5174646
sample estimates:
ratio of variances
1.788067 # Model Results
hull_t <- t.test(incoord.group$Hull_b, coord.group$Hull_b, var.equal = T)
hull_t
Two Sample t-test
data: incoord.group$Hull_b and coord.group$Hull_b
t = 2.4391, df = 38, p-value = 0.0195
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.084279 11.670189
sample estimates:
mean of x mean of y
34.14243 27.76520 VSD 25
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(Hull_bVSD_25[Incoord == 0]))
Shapiro-Wilk normality test
data: Hull_bVSD_25[Incoord == 0]
W = 0.93529, p-value = 0.1951with(AcousticData, shapiro.test(Hull_bVSD_25[Incoord == 1]))
Shapiro-Wilk normality test
data: Hull_bVSD_25[Incoord == 1]
W = 0.966, p-value = 0.6692## Equal Variance Check
res.ftest.vsd25 <- var.test(Hull_bVSD_25 ~ Incoord, data = AcousticData)
res.ftest.vsd25
F test to compare two variances
data: Hull_bVSD_25 by Incoord
F = 1.2396, num df = 19, denom df = 19, p-value = 0.6444
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4906358 3.1317059
sample estimates:
ratio of variances
1.239567 # Model Results
vsd25_t <- t.test(incoord.group$Hull_bVSD_25, coord.group$Hull_bVSD_25, var.equal = T)
vsd25_t
Two Sample t-test
data: incoord.group$Hull_bVSD_25 and coord.group$Hull_bVSD_25
t = 1.4368, df = 38, p-value = 0.159
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.234943 7.274708
sample estimates:
mean of x mean of y
17.36480 14.34491 VSD 50
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(Hull_bVSD_50[Incoord == 0]))
Shapiro-Wilk normality test
data: Hull_bVSD_50[Incoord == 0]
W = 0.87217, p-value = 0.01283with(AcousticData, shapiro.test(Hull_bVSD_50[Incoord == 1]))
Shapiro-Wilk normality test
data: Hull_bVSD_50[Incoord == 1]
W = 0.90708, p-value = 0.05609## Equal Variance Check
res.ftest.vsd50 <- var.test(Hull_bVSD_50 ~ Incoord, data = AcousticData)
res.ftest.vsd50
F test to compare two variances
data: Hull_bVSD_50 by Incoord
F = 0.57467, num df = 19, denom df = 19, p-value = 0.2363
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.2274606 1.4518708
sample estimates:
ratio of variances
0.5746681 # Model Results (Mann-Whitney U test conducted since assumption of normality is violated)
vsd50_MW <- wilcox.test(Hull_bVSD_50 ~ Incoord, data = AcousticData)
vsd50_MW
Wilcoxon rank sum exact test
data: Hull_bVSD_50 by Incoord
W = 151, p-value = 0.1918
alternative hypothesis: true location shift is not equal to 0VSD 75
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(Hull_bVSD_75[Incoord == 0]))
Shapiro-Wilk normality test
data: Hull_bVSD_75[Incoord == 0]
W = 0.69079, p-value = 3.017e-05with(AcousticData, shapiro.test(Hull_bVSD_75[Incoord == 1]))
Shapiro-Wilk normality test
data: Hull_bVSD_75[Incoord == 1]
W = 0.84373, p-value = 0.004193## Equal Variance Check
res.ftest.vsd75 <- var.test(Hull_bVSD_75 ~ Incoord, data = AcousticData)
res.ftest.vsd75
F test to compare two variances
data: Hull_bVSD_75 by Incoord
F = 1.1578, num df = 19, denom df = 19, p-value = 0.7527
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4582864 2.9252210
sample estimates:
ratio of variances
1.157838 # Model Results (Mann-Whitney U test conducted since assumption of normality is violated)
vsd75_MW <- wilcox.test(Hull_bVSD_75 ~ Incoord, data = AcousticData)
vsd75_MW
Wilcoxon rank sum exact test
data: Hull_bVSD_75 by Incoord
W = 160, p-value = 0.2888
alternative hypothesis: true location shift is not equal to 0Orthographic Transcription Scores
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(transAcc[Incoord == 0]))
Shapiro-Wilk normality test
data: transAcc[Incoord == 0]
W = 0.87029, p-value = 0.01189with(AcousticData, shapiro.test(transAcc[Incoord == 1]))
Shapiro-Wilk normality test
data: transAcc[Incoord == 1]
W = 0.91581, p-value = 0.0823## Equal Variance Check
res.ftest.OT <- var.test(transAcc ~ Incoord, data = AcousticData)
res.ftest.OT
F test to compare two variances
data: transAcc by Incoord
F = 1.082, num df = 19, denom df = 19, p-value = 0.8654
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4282721 2.7336414
sample estimates:
ratio of variances
1.082009 # Model Results (Mann-Whitney U test conducted since assumption of normality is violated)
OT_MW <- wilcox.test(transAcc ~ Incoord, data = AcousticData)
OT_MW
Wilcoxon rank sum exact test
data: transAcc by Incoord
W = 194, p-value = 0.8831
alternative hypothesis: true location shift is not equal to 0VAS
# Assumption Check
## Checking Normality
with(AcousticData, shapiro.test(VAS[Incoord == 0]))
Shapiro-Wilk normality test
data: VAS[Incoord == 0]
W = 0.87506, p-value = 0.01444with(AcousticData, shapiro.test(VAS[Incoord == 1]))
Shapiro-Wilk normality test
data: VAS[Incoord == 1]
W = 0.91494, p-value = 0.07923## Equal Variance Check
res.ftest.VAS <- var.test(VAS ~ Incoord, data = AcousticData)
res.ftest.VAS
F test to compare two variances
data: VAS by Incoord
F = 1.0744, num df = 19, denom df = 19, p-value = 0.8774
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4252539 2.7143765
sample estimates:
ratio of variances
1.074383 # Model Results (Mann-Whitney U test conducted since assumption of normality is violated)
OT_MW <- wilcox.test(VAS ~ Incoord, data = AcousticData)
OT_MW
Wilcoxon rank sum exact test
data: VAS by Incoord
W = 207, p-value = 0.862
alternative hypothesis: true location shift is not equal to 0OT Analysis
Since we found significant group differences for some acoustic measures between the ALS/PD and Ataxic/HD groups, we continued the heirarichal regression approach from OT Model 5. Adding in the Incoord predictor along with the interactions between the acoustic measures did not significantly improve model fit. So our original final OT model is retained.
Model 6
## Specifying Model 6
OT_Model6 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b + Incoord, data = AcousticData)
## Model 6 Assumption Check
performance::check_model(OT_Model6)
## Model 6 Summary
summary(OT_Model6)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + Incoord, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.105 -11.266 4.121 13.717 31.536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.5375 21.0257 0.834 0.4102
Hull_bVSD_25 -1.6809 1.1892 -1.413 0.1669
Hull_bVSD_75 0.9833 2.1390 0.460 0.6487
Hull_b 1.0629 0.7946 1.338 0.1901
VSA_b 6.5678 2.6475 2.481 0.0184 *
vowel_ED_b 4.8102 12.8358 0.375 0.7102
Incoord1 -12.8123 7.6496 -1.675 0.1034
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.02 on 33 degrees of freedom
Multiple R-squared: 0.346, Adjusted R-squared: 0.2271
F-statistic: 2.91 on 6 and 33 DF, p-value: 0.02173## Model 5 and Model 6 Comparison
anova(OT_Model5, OT_Model6)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord
Res.Df RSS Df Sum of Sq F Pr(>F)
1 34 15823
2 33 14583 1 1239.7 2.8053 0.1034Model 7
## Specifying Model 7
OT_Model7 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25, data = AcousticData)
## Model 7 Assumption Check
performance::check_model(OT_Model7)
## Model 7 Summary
summary(OT_Model7)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + Incoord + Incoord * Hull_bVSD_25, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.665 -10.983 4.212 14.044 31.363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.3827 22.9018 0.846 0.4036
Hull_bVSD_25 -1.7865 1.2973 -1.377 0.1780
Hull_bVSD_75 0.8920 2.2093 0.404 0.6891
Hull_b 1.0871 0.8136 1.336 0.1909
VSA_b 6.5883 2.6881 2.451 0.0199 *
vowel_ED_b 4.3440 13.1935 0.329 0.7441
Incoord1 -16.7140 19.2373 -0.869 0.3914
Hull_bVSD_25:Incoord1 0.2417 1.0905 0.222 0.8260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.33 on 32 degrees of freedom
Multiple R-squared: 0.347, Adjusted R-squared: 0.2042
F-statistic: 2.429 on 7 and 32 DF, p-value: 0.04086## Model 6 and Model 7 Comparison
anova(OT_Model6, OT_Model7)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25
Res.Df RSS Df Sum of Sq F Pr(>F)
1 33 14583
2 32 14561 1 22.358 0.0491 0.826Model 8
## Specifying Model 8
OT_Model8 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75, data = AcousticData)
## Model 8 Assumption Check
performance::check_model(OT_Model8)
## Model 8 Summary
summary(OT_Model8)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.858 -12.366 6.261 13.135 30.401
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.4909 22.7677 0.856 0.3985
Hull_bVSD_25 -1.4002 1.3310 -1.052 0.3009
Hull_bVSD_75 -0.7590 2.6079 -0.291 0.7730
Hull_b 0.9879 0.8132 1.215 0.2336
VSA_b 6.8700 2.6831 2.560 0.0155 *
vowel_ED_b 4.0042 13.1193 0.305 0.7622
Incoord1 -6.3149 21.0758 -0.300 0.7665
Hull_bVSD_25:Incoord1 -1.1508 1.6068 -0.716 0.4792
Hull_bVSD_75:Incoord1 5.4156 4.6125 1.174 0.2493
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.21 on 31 degrees of freedom
Multiple R-squared: 0.3748, Adjusted R-squared: 0.2135
F-statistic: 2.323 on 8 and 31 DF, p-value: 0.04412## Model 7 and Model 8 Comparison
anova(OT_Model7, OT_Model8)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75
Res.Df RSS Df Sum of Sq F Pr(>F)
1 32 14561
2 31 13941 1 619.94 1.3786 0.2493Model 9
## Specifying Model 9
OT_Model9 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75 + Incoord*Hull_b, data = AcousticData)
## Model 9 Assumption Check
performance::check_model(OT_Model9)
## Model 9 Summary
summary(OT_Model9)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75 + Incoord * Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-43.387 -11.747 5.389 14.794 30.552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.2613 23.3541 0.953 0.3481
Hull_bVSD_25 -0.8239 1.6003 -0.515 0.6104
Hull_bVSD_75 -1.1104 2.6847 -0.414 0.6821
Hull_b 0.5095 1.0933 0.466 0.6446
VSA_b 6.9293 2.7092 2.558 0.0158 *
vowel_ED_b 5.4227 13.4118 0.404 0.6888
Incoord1 -22.3892 32.2682 -0.694 0.4931
Hull_bVSD_25:Incoord1 -2.4414 2.5348 -0.963 0.3432
Hull_bVSD_75:Incoord1 6.0684 4.7580 1.275 0.2120
Hull_b:Incoord1 1.1098 1.6753 0.662 0.5128
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.4 on 30 degrees of freedom
Multiple R-squared: 0.3838, Adjusted R-squared: 0.199
F-statistic: 2.076 on 9 and 30 DF, p-value: 0.06468## Model 8 and Model 9 Comparison
anova(OT_Model8, OT_Model9)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 31 13941
2 30 13740 1 200.96 0.4388 0.5128Model 10
## Specifying Model 10
OT_Model10 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75 + Incoord*Hull_b + Incoord*VSA_b, data = AcousticData)
## Model 10 Assumption Check
performance::check_model(OT_Model10)
## Model 10 Summary
summary(OT_Model10)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75 + Incoord * Hull_b + Incoord * VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-43.360 -12.039 5.446 15.103 30.988
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.5101 23.8532 0.944 0.353
Hull_bVSD_25 -0.8290 1.6280 -0.509 0.614
Hull_bVSD_75 -1.1292 2.7353 -0.413 0.683
Hull_b 0.4795 1.1439 0.419 0.678
VSA_b 7.3071 4.3698 1.672 0.105
vowel_ED_b 5.0854 13.9703 0.364 0.718
Incoord1 -21.7310 33.3409 -0.652 0.520
Hull_bVSD_25:Incoord1 -2.3862 2.6248 -0.909 0.371
Hull_bVSD_75:Incoord1 6.0316 4.8495 1.244 0.224
Hull_b:Incoord1 1.1357 1.7194 0.660 0.514
VSA_b:Incoord1 -0.5224 4.6911 -0.111 0.912
Residual standard error: 21.76 on 29 degrees of freedom
Multiple R-squared: 0.3841, Adjusted R-squared: 0.1717
F-statistic: 1.809 on 10 and 29 DF, p-value: 0.1038## Model 9 and Model 10 Comparison
anova(OT_Model9, OT_Model10)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b + Incoord * VSA_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 30 13740
2 29 13734 1 5.8732 0.0124 0.9121Model 11
## Specifying Model 11
OT_Model11 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75 + Incoord*Hull_b + Incoord*VSA_b + Incoord*vowel_ED_b, data = AcousticData)
## Model 11 Assumption Check
performance::check_model(OT_Model11)
## Model 11 Summary
summary(OT_Model11)
Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b +
VSA_b + vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75 + Incoord * Hull_b + Incoord * VSA_b + Incoord *
vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-43.580 -9.987 5.535 12.978 34.285
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.2053 29.3160 1.474 0.1517
Hull_bVSD_25 -1.1058 1.6324 -0.677 0.5037
Hull_bVSD_75 -1.5475 2.7375 -0.565 0.5764
Hull_b 0.7017 1.1505 0.610 0.5469
VSA_b 10.1342 4.9387 2.052 0.0496 *
vowel_ED_b -11.6247 19.6753 -0.591 0.5594
Incoord1 -65.5861 49.3675 -1.329 0.1947
Hull_bVSD_25:Incoord1 -2.4987 2.6071 -0.958 0.3460
Hull_bVSD_75:Incoord1 7.0129 4.8830 1.436 0.1620
Hull_b:Incoord1 0.9481 1.7139 0.553 0.5845
VSA_b:Incoord1 -4.9394 5.9410 -0.831 0.4128
vowel_ED_b:Incoord1 33.2037 27.7349 1.197 0.2413
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.6 on 28 degrees of freedom
Multiple R-squared: 0.4141, Adjusted R-squared: 0.1839
F-statistic: 1.799 on 11 and 28 DF, p-value: 0.1025## Model 10 and Model 11 Comparison
anova(OT_Model10, OT_Model11)Analysis of Variance Table
Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b + Incoord * VSA_b
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b + Incoord * VSA_b + Incoord * vowel_ED_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 29 13734
2 28 13065 1 668.77 1.4332 0.2413VAS Analysis
Since we found significant group differences for some acoustic measures between the ALS/PD and Ataxic/HD groups, we continued the hierarchical regression approach from VAS Model 5. VAS Model 6 fit significantly better than VAS Model 5. However, adding in the interactions between Incoord and the acoustic measures did not significantly improve model fit.
Model 6
## Specifying Model 6
VAS_Model6 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b + Incoord, data = AcousticData)
## Model 6 Assumption Check
performance::check_model(VAS_Model6)
## Model 6 Summary
summary(VAS_Model6)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + Incoord, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.188 -13.751 2.167 16.256 36.926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.6850 22.7911 0.381 0.7056
Hull_bVSD_25 -2.1615 1.2891 -1.677 0.1030
Hull_bVSD_75 1.7100 2.3186 0.738 0.4660
Hull_b 1.2359 0.8613 1.435 0.1607
VSA_b 7.4668 2.8698 2.602 0.0138 *
vowel_ED_b 5.5718 13.9135 0.400 0.6914
Incoord1 -17.9628 8.2919 -2.166 0.0376 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.79 on 33 degrees of freedom
Multiple R-squared: 0.3632, Adjusted R-squared: 0.2474
F-statistic: 3.137 on 6 and 33 DF, p-value: 0.0152## Model 5 and Model 6 Comparison
anova(VAS_Model5, VAS_Model6)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord
Res.Df RSS Df Sum of Sq F Pr(>F)
1 34 19572
2 33 17135 1 2436.7 4.6929 0.03761 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Model 7
## Specifying Model 7
VAS_Model7 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25, data = AcousticData)
## Model 7 Assumption Check
performance::check_model(VAS_Model7)
## Model 7 Summary
summary(VAS_Model7)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + Incoord + Incoord * Hull_bVSD_25, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.039 -14.972 3.129 15.900 35.174
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.6424 24.7265 0.552 0.5850
Hull_bVSD_25 -2.4451 1.4006 -1.746 0.0905 .
Hull_bVSD_75 1.4646 2.3853 0.614 0.5435
Hull_b 1.3009 0.8784 1.481 0.1484
VSA_b 7.5218 2.9023 2.592 0.0143 *
vowel_ED_b 4.3193 14.2447 0.303 0.7637
Incoord1 -28.4451 20.7701 -1.370 0.1804
Hull_bVSD_25:Incoord1 0.6494 1.1773 0.552 0.5851
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.03 on 32 degrees of freedom
Multiple R-squared: 0.3692, Adjusted R-squared: 0.2312
F-statistic: 2.675 on 7 and 32 DF, p-value: 0.02675## Model 6 and Model 7 Comparison
anova(VAS_Model6, VAS_Model7)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25
Res.Df RSS Df Sum of Sq F Pr(>F)
1 33 17135
2 32 16973 1 161.37 0.3042 0.5851Model 8
## Specifying Model 8
VAS_Model8 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75, data = AcousticData)
## Model 8 Assumption Check
performance::check_model(VAS_Model8)
## Model 8 Summary
summary(VAS_Model8)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.267 -15.867 1.797 15.875 35.281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.7131 24.9259 0.550 0.5862
Hull_bVSD_25 -2.1928 1.4571 -1.505 0.1425
Hull_bVSD_75 0.3863 2.8551 0.135 0.8932
Hull_b 1.2361 0.8903 1.388 0.1749
VSA_b 7.7058 2.9374 2.623 0.0134 *
vowel_ED_b 4.0973 14.3629 0.285 0.7773
Incoord1 -21.6529 23.0737 -0.938 0.3553
Hull_bVSD_25:Incoord1 -0.2601 1.7591 -0.148 0.8834
Hull_bVSD_75:Incoord1 3.5372 5.0497 0.700 0.4889
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.22 on 31 degrees of freedom
Multiple R-squared: 0.379, Adjusted R-squared: 0.2188
F-statistic: 2.365 on 8 and 31 DF, p-value: 0.04086## Model 7 and Model 8 Comparison
anova(VAS_Model7, VAS_Model8)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75
Res.Df RSS Df Sum of Sq F Pr(>F)
1 32 16973
2 31 16709 1 264.47 0.4907 0.4889Model 9
## Specifying Model 9
VAS_Model9 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75 + Incoord*Hull_b, data = AcousticData)
## Model 9 Assumption Check
performance::check_model(VAS_Model9)
## Model 9 Summary
summary(VAS_Model9)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75 + Incoord * Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.224 -14.209 4.443 15.797 35.400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.9000 25.6576 0.620 0.5401
Hull_bVSD_25 -1.7379 1.7582 -0.988 0.3308
Hull_bVSD_75 0.1089 2.9495 0.037 0.9708
Hull_b 0.8585 1.2011 0.715 0.4803
VSA_b 7.7527 2.9764 2.605 0.0142 *
vowel_ED_b 5.2170 14.7346 0.354 0.7258
Incoord1 -34.3418 35.4508 -0.969 0.3404
Hull_bVSD_25:Incoord1 -1.2789 2.7848 -0.459 0.6494
Hull_bVSD_75:Incoord1 4.0525 5.2273 0.775 0.4443
Hull_b:Incoord1 0.8760 1.8406 0.476 0.6376
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.51 on 30 degrees of freedom
Multiple R-squared: 0.3837, Adjusted R-squared: 0.1988
F-statistic: 2.075 on 9 and 30 DF, p-value: 0.06485## Model 8 and Model 9 Comparison
anova(VAS_Model8, VAS_Model9)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 31 16709
2 30 16584 1 125.23 0.2265 0.6376Model 10
## Specifying Model 10
VAS_Model10 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75 + Incoord*Hull_b + Incoord*VSA_b, data = AcousticData)
## Model 10 Assumption Check
performance::check_model(VAS_Model10)
## Model 10 Summary
summary(VAS_Model10)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75 + Incoord * Hull_b + Incoord * VSA_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-41.129 -14.693 2.991 15.841 36.960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.79076 26.15188 0.642 0.5259
Hull_bVSD_25 -1.75601 1.78484 -0.984 0.3333
Hull_bVSD_75 0.04151 2.99886 0.014 0.9891
Hull_b 0.75103 1.25414 0.599 0.5539
VSA_b 9.10491 4.79089 1.900 0.0674 .
vowel_ED_b 4.00968 15.31664 0.262 0.7953
Incoord1 -31.98544 36.55389 -0.875 0.3888
Hull_bVSD_25:Incoord1 -1.08148 2.87770 -0.376 0.7098
Hull_bVSD_75:Incoord1 3.92085 5.31689 0.737 0.4668
Hull_b:Incoord1 0.96884 1.88513 0.514 0.6112
VSA_b:Incoord1 -1.87019 5.14318 -0.364 0.7188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.86 on 29 degrees of freedom
Multiple R-squared: 0.3865, Adjusted R-squared: 0.1749
F-statistic: 1.827 on 10 and 29 DF, p-value: 0.1001## Model 9 and Model 10 Comparison
anova(VAS_Model9, VAS_Model10)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b + Incoord * VSA_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 30 16584
2 29 16508 1 75.269 0.1322 0.7188Model 11
## Specifying Model 11
VAS_Model11 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord*Hull_bVSD_25 + Incoord*Hull_bVSD_75 + Incoord*Hull_b + Incoord*VSA_b + Incoord*vowel_ED_b, data = AcousticData)
## Model 11 Assumption Check
performance::check_model(VAS_Model11)
## Model 11 Summary
summary(VAS_Model11)
Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b +
vowel_ED_b + Incoord + Incoord * Hull_bVSD_25 + Incoord *
Hull_bVSD_75 + Incoord * Hull_b + Incoord * VSA_b + Incoord *
vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-40.891 -10.110 1.062 12.791 41.858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.5360 31.4458 1.512 0.1418
Hull_bVSD_25 -2.1672 1.7510 -1.238 0.2261
Hull_bVSD_75 -0.5799 2.9363 -0.198 0.8449
Hull_b 1.0811 1.2341 0.876 0.3885
VSA_b 13.3050 5.2975 2.512 0.0181 *
vowel_ED_b -20.8152 21.1048 -0.986 0.3324
Incoord1 -97.1376 52.9541 -1.834 0.0772 .
Hull_bVSD_25:Incoord1 -1.2486 2.7965 -0.447 0.6587
Hull_bVSD_75:Incoord1 5.3787 5.2378 1.027 0.3133
Hull_b:Incoord1 0.6902 1.8384 0.375 0.7102
VSA_b:Incoord1 -8.4322 6.3726 -1.323 0.1965
vowel_ED_b:Incoord1 49.3281 29.7498 1.658 0.1085
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.17 on 28 degrees of freedom
Multiple R-squared: 0.4413, Adjusted R-squared: 0.2218
F-statistic: 2.011 on 11 and 28 DF, p-value: 0.06657## Model 10 and Model 11 Comparison
anova(VAS_Model10, VAS_Model11)Analysis of Variance Table
Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b + Incoord * VSA_b
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b +
Incoord + Incoord * Hull_bVSD_25 + Incoord * Hull_bVSD_75 +
Incoord * Hull_b + Incoord * VSA_b + Incoord * vowel_ED_b
Res.Df RSS Df Sum of Sq F Pr(>F)
1 29 16508
2 28 15032 1 1476 2.7493 0.1085New Final VAS Model
Since VAS Model 6 was significantly better fit than Model 5, we fit a new final parsimonious model for VAS to the data (VAS ~ VSA_b + Incoord) and compared that to the old final VAS model (VAS ~ VSA_b). The new final model was not a significantly better fit than the old final model. Thus the old final model (VAS ~ VSA_b) is retained.
## Specifying New Final VAS Model
VAS_Model_newfinal <- lm(VAS ~ VSA_b + Incoord, data = AcousticData)
## New Final VAS Model Assumption Check
performance::check_model(VAS_Model_newfinal)
## New Final VAS Model Summary
summary(VAS_Model_newfinal)
Call:
lm(formula = VAS ~ VSA_b + Incoord, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-42.770 -17.018 3.197 18.896 40.044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.305 8.498 2.978 0.005097 **
VSA_b 7.680 1.925 3.990 0.000301 ***
Incoord1 -14.624 7.873 -1.858 0.071190 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.54 on 37 degrees of freedom
Multiple R-squared: 0.3012, Adjusted R-squared: 0.2635
F-statistic: 7.975 on 2 and 37 DF, p-value: 0.001319## Comparison to Old Final Model
anova(VAS_Model_final, VAS_Model_newfinal)Analysis of Variance Table
Model 1: VAS ~ VSA_b
Model 2: VAS ~ VSA_b + Incoord
Res.Df RSS Df Sum of Sq F Pr(>F)
1 38 20556
2 37 18802 1 1753.6 3.4509 0.07119 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Testing Individual Predictors
Because some correlations between predictors are high. We run a series of simple linear regressions to test if the predictors significantly predict each intelligibility measure on their own (VSD 25 and VSA models were already complete in our initial model comparison approach)
Orthographic Transcription Models
# OT ~ VSD 75
OT_vsd75_model <- lm(transAcc ~ Hull_bVSD_75, data = AcousticData)
performance::check_model(OT_vsd75_model)
summary(OT_vsd75_model)
Call:
lm(formula = transAcc ~ Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.60 -16.32 6.10 16.13 31.96
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 56.4278 5.4314 10.389 1.17e-12 ***
Hull_bVSD_75 0.9052 1.7124 0.529 0.6
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.14 on 38 degrees of freedom
Multiple R-squared: 0.0073, Adjusted R-squared: -0.01882
F-statistic: 0.2794 on 1 and 38 DF, p-value: 0.6001# OT ~ Hull
OT_hull_model <- lm(transAcc ~ Hull_b, data = AcousticData)
performance::check_model(OT_hull_model)
summary(OT_hull_model)
Call:
lm(formula = transAcc ~ Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-53.141 -12.306 4.848 18.324 31.854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.0817 13.5989 2.506 0.0166 *
Hull_b 0.7879 0.4231 1.862 0.0703 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.19 on 38 degrees of freedom
Multiple R-squared: 0.08365, Adjusted R-squared: 0.05953
F-statistic: 3.469 on 1 and 38 DF, p-value: 0.07029# OT ~ Corner Dispersion
OT_disp_model <- lm(transAcc ~ vowel_ED_b, data = AcousticData)
performance::check_model(OT_disp_model)
summary(OT_disp_model)
Call:
lm(formula = transAcc ~ vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-53.949 -10.348 4.268 14.982 25.903
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.227 18.611 0.335 0.73977
vowel_ED_b 25.564 8.946 2.857 0.00689 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.98 on 38 degrees of freedom
Multiple R-squared: 0.1769, Adjusted R-squared: 0.1552
F-statistic: 8.165 on 1 and 38 DF, p-value: 0.006891VAS Models
# VAS ~ VSD 75
VAS_vsd75_model <- lm(VAS ~ Hull_bVSD_75, data = AcousticData)
performance::check_model(VAS_vsd75_model)
summary(VAS_vsd75_model)
Call:
lm(formula = VAS ~ Hull_bVSD_75, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-47.036 -18.277 9.543 21.192 37.312
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.543 5.963 8.308 4.51e-10 ***
Hull_bVSD_75 1.067 1.880 0.567 0.574
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26.5 on 38 degrees of freedom
Multiple R-squared: 0.0084, Adjusted R-squared: -0.01769
F-statistic: 0.3219 on 1 and 38 DF, p-value: 0.5738# VAS ~ Hull
VAS_hull_model <- lm(VAS ~ Hull_b, data = AcousticData)
performance::check_model(VAS_hull_model)
summary(VAS_hull_model)
Call:
lm(formula = VAS ~ Hull_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-52.65 -19.68 7.88 21.88 33.56
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.3768 15.0913 1.880 0.0677 .
Hull_b 0.7616 0.4695 1.622 0.1130
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 25.73 on 38 degrees of freedom
Multiple R-squared: 0.06476, Adjusted R-squared: 0.04015
F-statistic: 2.631 on 1 and 38 DF, p-value: 0.113# VAS ~ Corner Dispersion
VAS_disp_model <- lm(VAS ~ vowel_ED_b, data = AcousticData)
performance::check_model(VAS_disp_model)
summary(VAS_disp_model)
Call:
lm(formula = VAS ~ vowel_ED_b, data = AcousticData)
Residuals:
Min 1Q Median 3Q Max
-52.146 -13.413 7.142 18.719 32.964
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.859 20.636 -0.139 0.8905
vowel_ED_b 26.819 9.920 2.704 0.0102 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 24.37 on 38 degrees of freedom
Multiple R-squared: 0.1613, Adjusted R-squared: 0.1393
F-statistic: 7.31 on 1 and 38 DF, p-value: 0.0102Listener Demographic Information
ListenerDemo <- Listeners %>%
furniture::table1(age, gender, race, ethnicity)ListenerDemo
────────────────────────────────────────────────────────────────────────────────────────────Speaker Demographics
SpeakerDemo <- AcousticData %>%
dplyr::select(c(Speaker, Sex, Etiology))
Ages <- rio::import("Prepped Data/Speaker Ages.xlsx")
SpeakerDemo <- full_join(SpeakerDemo, Ages, by = "Speaker")
SpeakerDemoInfo <- SpeakerDemo %>%
furniture::table1(Sex, Etiology, Age, na.rm = F)SpeakerDemoInfo
──────────────────────────────────────────────────────────SpeakerDemo %>%
dplyr::summarize(mean_age = mean(Age, na.rm = T), age_sd = sd(Age, na.rm = T), age_range = range(Age, na.rm = T))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